a case for end system multicast
DESCRIPTION
A Case For End System Multicast. Yang-hua Chu, Sanjay Rao and Hui Zhang Carnegie Mellon University. Unicast Transmission. Stanford. Gatech. CMU. Berkeley. End Systems. Routers. IP Multicast. Routers with multicast support. Gatech. Stanford. CMU. Berkeley. - PowerPoint PPT PresentationTRANSCRIPT
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A Case For End System Multicast
Yang-hua Chu, Sanjay Rao and Hui Zhang
Carnegie Mellon University
2
Unicast Transmission
End Systems
Routers
Gatech
CMU
Stanford
Berkeley
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IP Multicast
•No duplicate packets
•Highly efficient bandwidth usage
Key Architectural Decision: Add support for multicast in IP layer
Berkeley
Gatech Stanford
CMU
Routers with multicast support
4
Key Concerns with IP Multicast• Scalability with number of groups
– Routers maintain per-group state
– Analogous to per-flow state for QoS guarantees
– Aggregation of multicast addresses is complicated
• Supporting higher level functionality is difficult– IP Multicast: best-effort multi-point delivery service
– End systems responsible for handling higher level functionality
– Reliability and congestion control for IP Multicast complicated
• Deployment is difficult and slow– ISP’s reluctant to turn on IP Multicast
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The billion dollar question...• Can we achieve
efficient multi-point delivery,
without support from the IP layer?
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End System MulticastStanford
CMU
Stan1
Stan2
Berk2
Overlay TreeGatech
Berk1
Berkeley
Gatech Stan1
Stan2
Berk1
Berk2
CMU
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• Scalability– Routers do not maintain per-group state
– End systems do, but they participate in very few groups
• Easier to deploy
• Potentially simplifies support for higher level functionality– Leverage computation and storage of end systems
– For example, for buffering packets, transcoding, ACK aggregation
– Leverage solutions for unicast congestion control and reliability
Potential Benefits
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What I hope to convince you of ...
• End System Multicast is a promising alternative approach for multi-point delivery– Narada: A distributed protocol for constructing efficient overlay
trees among end systems
– Simulation and Internet evaluation results to demonstrate that Narada can achieve good performance
• Consider applications with small and sparse groups – Around tens to hundreds of members
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Performance Concerns
CMU
Gatech Stan1
Stan2
Berk1
Berk2
Duplicate Packets:
Bandwidth Wastage
CMU
Stan1
Stan2
Berk2
Gatech
Berk1
Delay from CMU to
Berk1 increases
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What is an efficient overlay tree?• The delay between the source and receivers is small
• Ideally,
– The number of redundant packets on any physical link is low
Heuristic we use:
– Every member in the tree has a small degree
– Degree chosen to reflect bandwidth of connection to Internet
Gatech
“Efficient” overlay
CMU
Berk2
Stan1
Stan2
Berk1Berk1
High degree (unicast)
Berk2
Gatech
Stan2CMU
Stan1
Stan2
High latency
CMU
Berk2
Gatech
Stan1
Berk1
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Why is self-organization hard?
• Dynamic changes in group membership – Members may join and leave dynamically
– Members may die
• Limited knowledge of network conditions– Members do not know delay to each other when they join
– Members probe each other to learn network related information
– Overlay must self-improve as more information available
• Dynamic changes in network conditions – Delay between members may vary over time due to congestion
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Berk2 Berk1
CMU
Gatech
Stan1Stan2
Narada Design
CMU
Berk2 GatechBerk1
Stan1Stan2
Step 1
•Source rooted shortest delay spanning trees of mesh
•Constructed using well known routing algorithms– Members have low degrees
– Small delay from source to receivers
“Mesh”: Richer overlay that may have cycles and
includes all group members• Members have low degrees
• Shortest path delay between any pair of members along mesh is small
Step 2
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Narada Components• Mesh Management:
– Ensures mesh remains connected in face of membership changes
• Mesh Optimization:– Distributed heuristics for ensuring shortest path delay between
members along the mesh is small
• Spanning tree construction:– Routing algorithms for constructing data-delivery trees
– Distance vector routing, and reverse path forwarding
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Optimizing Mesh Quality
• Members periodically probe other members at random
• New Link added ifUtility Gain of adding link > Add Threshold
• Members periodically monitor existing links
• Existing Link dropped ifCost of dropping link < Drop Threshold
Berk1
Stan2CMU
Gatech1
Stan1
Gatech2
A poor overlay topology
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The terms defined • Utility gain of adding a link based on
– The number of members to which routing delay improves
– How significant the improvement in delay to each member is
• Cost of dropping a link based on– The number of members to which routing delay increases, for
either neighbor
• Add/Drop Thresholds are functions of:– Member’s estimation of group size
– Current and maximum degree of member in the mesh
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Desirable properties of heuristics
• Stability: A dropped link will not be immediately readded
• Partition Avoidance: A partition of the mesh is unlikely to be caused as a result of any single link being dropped
Delay improves to Stan1, CMU
but marginally.
Do not add link!
Delay improves to CMU, Gatech1
and significantly.
Add link!
Berk1
Stan2CMU
Gatech1
Stan1
Gatech2
Probe
Berk1
Stan2CMU
Gatech1
Stan1
Gatech2Probe
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Used by Berk1 to reach only Gatech2 and vice versa.
Drop!!
An improved mesh !!
Gatech1Berk1
Stan2CMU
Stan1
Gatech2
Gatech1Berk1
Stan2CMU
Stan1
Gatech2
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Narada Evaluation
• Simulation experiments
• Evaluation of an implementation on the Internet
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Performance Metrics
• Delay between members using Narada
• Stress, defined as the number of identical copies of a packet that traverse a physical link
Berk2
Gatech Stan1Stress = 2
CMU
Stan2
Berk1
Berk2CMU
Stan1
Stan2Gatech
Berk1
Delay from CMU to Delay from CMU to
Berk1 increasesBerk1 increases
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Factors affecting performance• Topology Model
– Waxman Variant – Mapnet: Connectivity modeled after several ISP backbones – ASMap: Based on inter-domain Internet connectivity
• Topology Size– Between 64 and 1024 routers
• Group Size– Between 16 and 256
• Fanout range– Number of neighbors each member tries to maintain in the mesh
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Simulation Details• Simulator
– Packet-level and event-based
– Models propagation delay of physical links
– Does not model queuing delay and packet loss
• Individual Experiment Description– All group members join in random sequence in first 100 seconds
– No change in group membership after 100 seconds
– One sender picked at random and multicasts data at constant rate
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Delay in typical run4 x unicast delay 1x unicast delay
Waxman : 1024 routers, 3145 linksGroup Size : 128 Fanout Range : <3-6> for all members
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Naive Unicast
Native Multicast
Narada : 14-fold reduction in
worst-case stress !
Stress in typical run
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Variation with group size
Waxman model: 1024 routers, 3145 links
Fanout Range: <3-6>
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Variation with topology model
Waxman
ASMapMapnet
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Implementation Status
• Implemented and ported to Linux and Sun
• Available as library that can be compiled with applications
• Examining how applications written with IP Multicast API can be used without source-code modification
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Internet Evaluation • 13 hosts, all join the group at about the same time
• No further change in group membership
• Each member tries to maintain 2-4 neighbors in the mesh
• Host at CMU designated source
Berkeley
UCSB
UIUC1
UIUC2 CMU1
CMU2
UKY
UMass
GATech
UDelVirginia1
Virginia2
UWisc
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31
1
10
13
15
14
111
381
10
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Narada Delay Vs. Unicast Delay
Internet Routing
can be sub-optimal
(ms)
(ms)
2x unicast delay 1x unicast delay
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Related Work
• Yoid (Paul Francis, ACIRI)– More emphasis on architectural aspects, less on performance
– Uses a shared tree among participating members
• More susceptible to a central point of failure
• Distributed heuristics for managing and optimizing a tree are more complicated as cycles must be avoided
• Scattercast (Chawathe et al, UC Berkeley)– Emphasis on infrastructural support and proxy-based multicast
• To us, an end system includes the notion of proxies
– Also uses a mesh, but differences in protocol details
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Conclusions
• Proposed in 1989, IP Multicast is not yet widely deployed– Per-group state, control state complexity and scaling concerns
– Difficult to support higher layer functionality
– Difficult to deploy, and get ISP’s to turn on IP Multicast
• Is IP the right layer for supporting multicast functionality?
• For small-sized groups, an end-system overlay approach – is feasible
– has a low performance penalty compared to IP Multicast
– has the potential to simplify support for higher layer functionality
– allows for application-specific customizations